Education

• 
  Shanghai Jiao Tong University (SJTU)

  School of Electronic Information and Electrical Engineering
  B.E. in Automation

  Sep. 2015 - Jul. 2019
• 
  University of Chinese Academy of Sciences (UCAS)

  Institute of Automation
  M.S. in Automation

  Sep. 2019 - Jul. 2022

Research Experience

• 
  Autonomous System Group, Intel Labs China (ILC)

  Research Intern

  Aug. 2020 - Dec. 2021
• 
  Institute of Automation, Chinese Academy of Sciences (CASIA)

  Student Researcher

  Sep. 2019 - Jul. 2022
• 
  Institute for AI Industry Research, Tsinghua University (AIR, THU)

  Researcher

  Jul. 2022 - Present

Academic Service

• Program Committee & Area Chair

  Workshop on Multi-Agent Embodied Intelligent Systems Meet Generative-AI Era @ CVPR'25
  Workshop on Cooperative Intelligence for Embodied AI @ ECCV'24

• Journal Reviewer

  IEEE (TIP, TCSVT, TVT, TIV, ITSM), IET (CV, CSR), PR, Neurocomputing

• Conference Reviewer

  CVPR, ICCV, ECCV, ICRA, IROS, ITSC

• Invited Speaker

  Traffic scenes understanding and simulation testing @ ITSC'22

Honors & Awards

• National Scholarship

  2021
• Pan Deng First-class Scholarship, CAS

  2022
• Excellent Scholarship, SJTU

  2018
• China Industrial Intelligence Challenge, State-level Outstanding Award, CAA

  2018

Autonomous Driving (AD)

• Onboard System (Intelligent Vehicle)

  My exploration of vehicle-side environment perception began with drivable area detection (ITSC’20). Since then, I have proposed a series of perception algorithms, including an RGB 2D object detection approach designed for complex traffic environments (FII-CenterNet, T-VT’21), a semi-supervised learning approach for RGB 2D segmentation (CPCL, T-IP’22), an RGB-T segmentation approach tailored for challenging lighting conditions (SpiderMesh, TechReport’23), and a 3D segmentation approach for large-scale point clouds (SCF-Net, CVPR’21).

• Roadside System (Intelligent Infrastructure)

  Compared to the well-studied vehicle-side perception, roadside perception faces several unique challenges, and the calibration noise caused by inevitable natural factors is one of them. Addressing that, a calibration-free BEV representation network is proposed to alleviate inaccurate calibration parameter problem (CBR, IROS’23). The development of roaside perception has been hindered by a lack of available data. On the one hand, a semantic-geometry decoupled contrastive learning framework is introduced to enhance roadside perception performance by leveraging vehicle-side data (IROAM, ICRA’25); On the other hand, the first large-scale real-world dataset for roadside cooperative perception is released, complete with benchmarks, to stimulate research in practical I2I perception (RCooper, CVPR’24).

• Cooperative Autonomous Driving System (V2X)

  Cooperative perception can significantly enhance individual perception performance by providing additional viewpoints and expanding the sensing field. A scene-level feature cooperative perception approach is proposed (EMIFF, ICRA’24). To enable interpretable, instance-level, and flexible feature interactions, the concept of query cooperation is introduced, along with a cooperative perception framework that allows query streams to flow among agents (QUEST, ICRA’24). Additionally, motion forecasting can also benefit from learning cooperative trajectory representations (NeurIPS’24). Beyond focusing on improving individual modules, a pioneering end-to-end cooperative autonomous driving framework is introduced (UniV2X, AAAI’25).

Biomedical Discovery (BD)

• Biomedical Agent System

  The microscopic biological system is intriguing but challenging. An all-atom framework is explored to enable consistent representation and interaction modeling across different biomolecules (PharMolixFM, TechReport’25).

• Human-Agent Interaction System

  Recent advances in LLMs have illuminated the path toward developing knowledgeable and versatile AI research assistants across various scientific domains. Multimodal large language models are particularly promising, as they bridge the semantic gap between natural language and other modalities such as molecules, proteins, and visual information. In this context, a multimodal large language model is proposed to assist biomedical research (BioMedGPT, J-BHI’24). Additionally, the task of optical chemical structure understanding is introduced and explored to facilitate molecule-centric scientific discovery (OCSU, TechReport’25).

• Multi-Agent Cooperation System

  Multi-agent cooperation holds great potential for solving complex scientific research tasks in an autonomous manner. To facilitate further exploration in this area, an agent platform specifically designed for biomedicine and life science is presented and open-sourced (OpenBioMed).